세계 합성 데이터 생성 시장 예측(-2030년) : 컴포넌트별, 전개 모드별, 제공별, 모델링 유형별, 데이터 유형별, 용도별, 최종 사용자별, 지역별 분석

According to Stratistics MRC, the Global Synthetic Data Generation Market is accounted for $372.45 million in 2023 and is expected to reach $2226.16 million by 2030 growing at a CAGR of 29.1% during the forecast period. The process of creating artificial datasets devoid of any personally identifiable information that closely resembles the statistical traits and patterns of real-world data is known as synthetic data generation. This procedure is especially helpful in a variety of domains, like machine learning, where having access to sizable and varied datasets is essential for testing and training models.

According to the American Medical Association, implementing comprehensive healthcare policies is essential for ensuring equitable access to quality medical services and addressing the diverse needs of patients across different demographic groups.

Market Dynamics:

Driver:

Growing requirement for various training datasets

The demand for broad and varied datasets to train reliable and accurate models has increased due to the exponential rise in machine learning applications across industries. Additionally, this need is met by synthetic data generation, which offers a scalable way to produce diverse datasets, facilitating more successful and efficient machine learning algorithm training procedures.

Restraint:

Absence of evaluation metrics and standards

The lack of established procedures for creating and analyzing synthetic data makes it difficult to judge the appropriateness and caliber of datasets that have been created artificially. Furthermore, it is imperative to establish metrics that are universally recognized in order to assess the efficacy and dependability of synthetic data and guarantee transparent and uniform practices across various industries and applications.

Opportunity:

Personalization for particular use cases

The customization of synthetic data generation for particular use cases represents a significant opportunity. More efficient training and testing of machine learning models is possible when synthetic datasets are designed to closely resemble specific industries, applications, or research domains. Moreover, this provides a level of specificity that may be difficult to attain with real-world data alone.

Threat:

Insufficient representativeness and amplification of bias

The potential inadequacy of capturing the true diversity and complexity of real-world data poses a serious threat to the creation of synthetic data. Synthetic datasets can introduce biases or fail to capture particular nuances found in the target domain if they are not carefully designed. Additionally, this can result in models that do not generalize well and can even reinforce preexisting biases.

Covid-19 Impact:

Due to its impact on demand and operational dynamics, the COVID-19 pandemic has had a major effect on the synthetic data generation market. On the one hand, the demand for cutting-edge technologies, such as synthetic data, to support machine learning development remotely has increased due to the growing emphasis on remote work and digital transformation. However, some organizations have re-evaluated their investments due to budgetary constraints and economic uncertainties, which may slow down market growth. Industry disruptions caused by the pandemic have also highlighted the value of synthetic data in situations where real-world data is either unobtainable or impractical.

The Predictive Analytics segment is expected to be the largest during the forecast period

During the projected period, the predictive analytics segment is expected to hold the largest market share. With the use of statistical algorithms, machine learning techniques, and historical and current data, predictive analytics helps businesses anticipate future events and outcomes by spotting patterns and trends. Furthermore, this market has grown in popularity in a number of sectors, such as marketing, e-commerce, finance, and healthcare, as companies learn more and more about the benefits of making proactive decisions based on data-driven insights.

The BFSI segment is expected to have the highest CAGR during the forecast period

The industry's highest CAGR is anticipated for the BFSI (banking, financial services, and insurance) sector. Synthetic data is becoming a more vital solution for model training and validation as the BFSI industry struggles to share sensitive financial and customer data for testing and development. Additionally, applications in BFSI include risk assessment, fraud detection, and compliance testing. Synthetic data promotes innovation while guaranteeing adherence to data privacy regulations.

Region with largest share:

It is projected that North America will command the largest market share. The early adoption of cutting-edge technologies, the robust presence of major industry players, and the development of an advanced ecosystem for machine learning and artificial intelligence applications are all factors contributing to the region's dominance. Moreover, in large part due to the use of synthetic data for model development, testing, and training by sectors including technology, healthcare, finance, and automotive, the synthetic data market has grown significantly in the United States.

Region with highest CAGR:

In the market for synthetic data generation, Asia-Pacific is anticipated to have the highest CAGR. The robust growth in demand for synthetic data is partly explained by the region's increasing investments in artificial intelligence, rapid adoption of emerging technologies, and growing presence of tech-driven industries. Furthermore, applications in industries including healthcare, finance, manufacturing, and retail are increasing in nations like China, India, Japan, and South Korea, creating a good environment for synthetic data solutions.

Key players in the market

Some of the key players in Synthetic Data Generation market include IBM, Google, AWS, TonicAI, Inc, Hazy Limited, Microsoft, Gretel Labs, Inc, Replica Analytics Ltd, Datagen, Informatica, GenRocket, Inc, YData Labs Inc, TCS and Replica Analytics Ltd.

Key Developments:

In January 2024, Google India Digital Services and NPCI International Payments (NIPL), a wholly-owned subsidiary of the National Payments Corporation of India (NPCI) have signed a Memorandum of Understanding (MoU) to enable UPI transactions outside India. The MoU seeks to broaden the use of UPI payments for Indian travellers to make transactions abroad. It also aims to establish UPI-like digital payment systems in other countries, providing a model for seamless financial transactions.

In January 2024, Amazon Web Services (AWS) looks set to make more money on three multi-million pound government contracts that went live on the same day in December 2023 than it has previously amassed through its decade-long involvement with the G-Cloud procurement framework. The public cloud giant signed three 36-month contracts with several different major government departments that all went live on 1 December 2023, including one valued at £350m with HM Revenue and Customs and another worth £94m with the Department for Work and Pensions.

In January 2024, Microsoft and Vodafone announced a significant 10-year strategic partnership aimed at driving digital transformation for businesses and consumers across Europe and Africa, leveraging their combined strengths in technology and connectivity. The collaboration will focus on enhancing Vodafone's customer experience through Microsoft's AI, expanding Vodafone's managed IoT connectivity platform, developing new digital and financial services for SMEs, and revamping Vodafone's global data center strategy.

Components Covered:

• Solution/Platform
• Services
• Other Components

Deployment Modes Covered:

• On-Premise
• Cloud

Offerings Covered:

• Fully Synthetic Data
• Partially Synthetic Data
• Hybrid Synthetic Data
• Other Offerings

Modeling Types Covered:

• Direct Modeling
• Agent-based Modeling
• Other Modeling Types

Data Types Covered:

• Tabular Data
• Text data
• Image and Video Data
• Other Data Types

Applications Covered:

• Data Protection
• Data Sharing
• Predictive Analytics
• Natural Language Processing
• Computer Vision Algorithms
• Other Applications

End Users Covered:

• BFSI
• Healthcare & Life sciences
• Retail and E-commerce
• Automotive and Transportation
• Government & Defense
• IT and ITeS
• Manufacturing
• Other End Users

Regions Covered:

• North America
  • US
  • Canada
  • Mexico
• Europe
  • Germany
  • UK
  • Italy
  • France
  • Spain
  • Rest of Europe
• Asia Pacific
  • Japan
  • China
  • India
  • Australia
  • New Zealand
  • South Korea
  • Rest of Asia Pacific
• South America
  • Argentina
  • Brazil
  • Chile
  • Rest of South America
• Middle East & Africa
  • Saudi Arabia
  • UAE
  • Qatar
  • South Africa
  • Rest of Middle East & Africa

What our report offers:

• Market share assessments for the regional and country-level segments
• Strategic recommendations for the new entrants
• Covers Market data for the years 2021, 2022, 2023, 2026, and 2030
• Market Trends (Drivers, Constraints, Opportunities, Threats, Challenges, Investment Opportunities, and recommendations)
• Strategic recommendations in key business segments based on the market estimations
• Competitive landscaping mapping the key common trends
• Company profiling with detailed strategies, financials, and recent developments
• Supply chain trends mapping the latest technological advancements

Free Customization Offerings:

All the customers of this report will be entitled to receive one of the following free customization options:

• Company Profiling
  • Comprehensive profiling of additional market players (up to 3)
  • SWOT Analysis of key players (up to 3)
• Regional Segmentation
  • Market estimations, Forecasts and CAGR of any prominent country as per the client's interest (Note: Depends on feasibility check)
• Competitive Benchmarking
  • Benchmarking of key players based on product portfolio, geographical presence, and strategic alliances

Table of Contents

1 Executive Summary

2 Preface

• 2.1 Abstract
• 2.2 Stake Holders
• 2.3 Research Scope
• 2.4 Research Methodology
  • 2.4.1 Data Mining
  • 2.4.2 Data Analysis
  • 2.4.3 Data Validation
  • 2.4.4 Research Approach
• 2.5 Research Sources
  • 2.5.1 Primary Research Sources
  • 2.5.2 Secondary Research Sources
  • 2.5.3 Assumptions

3 Market Trend Analysis

• 3.1 Introduction
• 3.2 Drivers
• 3.3 Restraints
• 3.4 Opportunities
• 3.5 Threats
• 3.6 Application Analysis
• 3.7 End User Analysis
• 3.8 Emerging Markets
• 3.9 Impact of Covid-19

4 Porters Five Force Analysis

• 4.1 Bargaining power of suppliers
• 4.2 Bargaining power of buyers
• 4.3 Threat of substitutes
• 4.4 Threat of new entrants
• 4.5 Competitive rivalry

5 Global Synthetic Data Generation Market, By Component

• 5.1 Introduction
• 5.2 Solution/Platform
• 5.3 Services
• 5.4 Other Components

6 Global Synthetic Data Generation Market, By Deployment Mode

• 6.1 Introduction
• 6.2 On-Premise
• 6.3 Cloud

7 Global Synthetic Data Generation Market, By Offering

• 7.1 Introduction
• 7.2 Fully Synthetic Data
• 7.3 Partially Synthetic Data
• 7.4 Hybrid Synthetic Data
• 7.5 Other Offerings

8 Global Synthetic Data Generation Market, By Modeling Type

• 8.1 Introduction
• 8.2 Direct Modeling
• 8.3 Agent-based Modeling
• 8.4 Other Modeling Types

9 Global Synthetic Data Generation Market, By Data Type

• 9.1 Introduction
• 9.2 Tabular Data
• 9.3 Text data
• 9.4 Image and Video Data
• 9.5 Other Data Types

10 Global Synthetic Data Generation Market, By Application

• 10.1 Introduction
• 10.2 Data Protection
• 10.3 Data Sharing
• 10.4 Predictive Analytics
• 10.5 Natural Language Processing
• 10.6 Computer Vision Algorithms
• 10.7 Other Applications

11 Global Synthetic Data Generation Market, By End User

• 11.1 Introduction
• 11.2 BFSI
• 11.3 Healthcare & Life sciences
• 11.4 Retail and E-commerce
• 11.5 Automotive and Transportation
• 11.6 Government & Defense
• 11.7 IT and ITeS
• 11.8 Manufacturing
• 11.9 Other End Users

12 Global Synthetic Data Generation Market, By Geography

• 12.1 Introduction
• 12.2 North America
  • 12.2.1 US
  • 12.2.2 Canada
  • 12.2.3 Mexico
• 12.3 Europe
  • 12.3.1 Germany
  • 12.3.2 UK
  • 12.3.3 Italy
  • 12.3.4 France
  • 12.3.5 Spain
  • 12.3.6 Rest of Europe
• 12.4 Asia Pacific
  • 12.4.1 Japan
  • 12.4.2 China
  • 12.4.3 India
  • 12.4.4 Australia
  • 12.4.5 New Zealand
  • 12.4.6 South Korea
  • 12.4.7 Rest of Asia Pacific
• 12.5 South America
  • 12.5.1 Argentina
  • 12.5.2 Brazil
  • 12.5.3 Chile
  • 12.5.4 Rest of South America
• 12.6 Middle East & Africa
  • 12.6.1 Saudi Arabia
  • 12.6.2 UAE
  • 12.6.3 Qatar
  • 12.6.4 South Africa
  • 12.6.5 Rest of Middle East & Africa

13 Key Developments

• 13.1 Agreements, Partnerships, Collaborations and Joint Ventures
• 13.2 Acquisitions & Mergers
• 13.3 New Product Launch
• 13.4 Expansions
• 13.5 Other Key Strategies

14 Company Profiling

• 14.1 IBM
• 14.2 Google
• 14.3 AWS
• 14.4 TonicAI, Inc
• 14.5 Hazy Limited
• 14.6 Microsoft
• 14.7 Gretel Labs, Inc
• 14.8 Replica Analytics Ltd
• 14.9 Datagen
• 14.10 Informatica
• 14.11 GenRocket, Inc
• 14.12 YData Labs Inc
• 14.13 TCS
• 14.14 Replica Analytics Ltd